Baum, Zachary MC;
Hu, Yipeng;
Barratt, Dean C;
(2022)
Meta-registration: Learning Test-Time Optimization for Single-Pair Image Registration.
In:
International Workshop on Advances in Simplifying Medical Ultrasound ASMUS 2022: Simplifying Medical Ultrasound.
(pp. pp. 162-171).
Springer, Cham
Preview |
Text
2207.10996.pdf - Accepted Version Download (497kB) | Preview |
Abstract
Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These trained networks can further be optimized on a single pair of test images - known as test-time optimization. This work formulates image registration as a meta-learning algorithm. Such networks can be trained by aligning the training image pairs while simultaneously improving test-time optimization efficacy; tasks which were previously considered two independent training and optimization processes. The proposed meta-registration is hypothesized to maximize the efficiency and effectiveness of the test-time optimization in the “outer” meta-optimization of the networks. For image guidance applications that often are time-critical yet limited in training data, the potentially gained speed and accuracy are compared with classical registration algorithms, registration networks without meta-learning, and single-pair optimization without test-time optimization data. Experiments are presented in this paper using clinical transrectal ultrasound image data from 108 prostate cancer patients. These experiments demonstrate the effectiveness of a meta-registration protocol, which yields significantly improved performance relative to existing learning-based methods. Furthermore, the meta-registration achieves comparable results to classical iterative methods in a fraction of the time, owing to its rapid test-time optimization process.
| Type: | Proceedings paper |
|---|---|
| Title: | Meta-registration: Learning Test-Time Optimization for Single-Pair Image Registration |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-16902-1_16 |
| Publisher version: | https://doi.org/10.1007/978-3-031-16902-1_16 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Image registration, Meta-learning, Deep learning, Ultrasound |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10153271 |
Archive Staff Only
 |
View Item |
